





'''simple docstring'''


import collections
import inspect
import unittest

from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
       from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor

class  lowerCAmelCase_		:




 def __init__(						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase=13      ,  _lowerCAmelCase=32      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=16      ,  _lowerCAmelCase=[1, 2, 1]      ,  _lowerCAmelCase=[2, 2, 4]      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=2.0      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=1E-5      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=10      ,  _lowerCAmelCase=8      ,  )     ->				Dict:
     _lowerCAmelCase						      =   parent
     _lowerCAmelCase						      =   batch_size
     _lowerCAmelCase						      =   image_size
     _lowerCAmelCase						      =   patch_size
     _lowerCAmelCase						      =   num_channels
     _lowerCAmelCase						      =   embed_dim
     _lowerCAmelCase						      =   depths
     _lowerCAmelCase						      =   num_heads
     _lowerCAmelCase						      =   window_size
     _lowerCAmelCase						      =   mlp_ratio
     _lowerCAmelCase						      =   qkv_bias
     _lowerCAmelCase						      =   hidden_dropout_prob
     _lowerCAmelCase						      =   attention_probs_dropout_prob
     _lowerCAmelCase						      =   drop_path_rate
     _lowerCAmelCase						      =   hidden_act
     _lowerCAmelCase						      =   use_absolute_embeddings
     _lowerCAmelCase						      =   patch_norm
     _lowerCAmelCase						      =   layer_norm_eps
     _lowerCAmelCase						      =   initializer_range
     _lowerCAmelCase						      =   is_training
     _lowerCAmelCase						      =   scope
     _lowerCAmelCase						      =   use_labels
     _lowerCAmelCase						      =   type_sequence_label_size
     _lowerCAmelCase						      =   encoder_stride




 def 				_snake_case (						self		)     ->				List[Any]:
     _lowerCAmelCase						      =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

     _lowerCAmelCase						      =   None
     if self.use_labels:
         _lowerCAmelCase						      =   ids_tensor([self.batch_size]      ,  self.type_sequence_label_size		)

     _lowerCAmelCase						      =   self.get_config()

     return config, pixel_values, labels




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     return SwinvaConfig(
         image_size=self.image_size      ,  patch_size=self.patch_size      ,  num_channels=self.num_channels      ,  embed_dim=self.embed_dim      ,  depths=self.depths      ,  num_heads=self.num_heads      ,  window_size=self.window_size      ,  mlp_ratio=self.mlp_ratio      ,  qkv_bias=self.qkv_bias      ,  hidden_dropout_prob=self.hidden_dropout_prob      ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,  drop_path_rate=self.drop_path_rate      ,  hidden_act=self.hidden_act      ,  use_absolute_embeddings=self.use_absolute_embeddings      ,  path_norm=self.patch_norm      ,  layer_norm_eps=self.layer_norm_eps      ,  initializer_range=self.initializer_range      ,  encoder_stride=self.encoder_stride      ,  )




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   SwinvaModel(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(_lowerCAmelCase		)

     _lowerCAmelCase						      =   ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths		) - 1))
     _lowerCAmelCase						      =   int(config.embed_dim * 2 ** (len(config.depths		) - 1)		)

     self.parent.assertEqual(result.last_hidden_state.shape      ,  (self.batch_size, expected_seq_len, expected_dim)		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Tuple:
     _lowerCAmelCase						      =   SwinvaForMaskedImageModeling(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(_lowerCAmelCase		)
     self.parent.assertEqual(
         result.logits.shape      ,  (self.batch_size, self.num_channels, self.image_size, self.image_size)		)

     # test greyscale images
     _lowerCAmelCase						      =   1
     _lowerCAmelCase						      =   SwinvaForMaskedImageModeling(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()

     _lowerCAmelCase						      =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
     _lowerCAmelCase						      =   model(_lowerCAmelCase		)
     self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, 1, self.image_size, self.image_size)		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Optional[int]:
     _lowerCAmelCase						      =   self.type_sequence_label_size
     _lowerCAmelCase						      =   SwinvaForImageClassification(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  labels=_lowerCAmelCase		)
     self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.type_sequence_label_size)		)





 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase						      =   self.prepare_config_and_inputs()
     _lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase						      =   config_and_inputs
     _lowerCAmelCase						      =   {"pixel_values": pixel_values}
     return config, inputs_dict







@require_torch
class  lowerCAmelCase_		(		__magic_name__    ,__magic_name__    ,unittest.TestCase		):
 __lowerCamelCase    :				Optional[int]								=       (
     (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
 )
 __lowerCamelCase    :				Any								=       (
     {"feature-extraction": SwinvaModel, "image-classification": SwinvaForImageClassification}
     if is_torch_available()
     else {}
 )

 __lowerCamelCase    :				Optional[int]								=       False
 __lowerCamelCase    :				List[Any]								=       False
 __lowerCamelCase    :				Dict								=       False
 __lowerCamelCase    :				str								=       False




 def 				_snake_case (						self		)     ->				Optional[int]:
     _lowerCAmelCase						      =   SwinvaModelTester(self		)
     _lowerCAmelCase						      =   ConfigTester(self      ,  config_class=_lowerCAmelCase      ,  embed_dim=37		)




 def 				_snake_case (						self		)     ->				int:
     self.config_tester.create_and_test_config_to_json_string()
     self.config_tester.create_and_test_config_to_json_file()
     self.config_tester.create_and_test_config_from_and_save_pretrained()
     self.config_tester.create_and_test_config_with_num_labels()
     self.config_tester.check_config_can_be_init_without_params()
     self.config_tester.check_config_arguments_init()




 def 				_snake_case (						self		)     ->				Tuple:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowerCAmelCase		)




 @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."		)
 def 				_snake_case (						self		)     ->				Optional[int]:
     pass




 @unittest.skip(reason="Swinv2 does not use inputs_embeds"		)
 def 				_snake_case (						self		)     ->				Tuple:
     pass




 def 				_snake_case (						self		)     ->				List[Any]:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         _lowerCAmelCase						      =   model_class(_lowerCAmelCase		)
         self.assertIsInstance(model.get_input_embeddings()      ,  (nn.Module)		)
         _lowerCAmelCase						      =   model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(_lowerCAmelCase      ,  nn.Linear		)		)




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         _lowerCAmelCase						      =   model_class(_lowerCAmelCase		)
         _lowerCAmelCase						      =   inspect.signature(model.forward		)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         _lowerCAmelCase						      =   [*signature.parameters.keys()]

         _lowerCAmelCase						      =   ["pixel_values"]
         self.assertListEqual(arg_names[:1]      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				int:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs_for_common()
     _lowerCAmelCase						      =   True

     for model_class in self.all_model_classes:
         _lowerCAmelCase						      =   True
         _lowerCAmelCase						      =   False
         _lowerCAmelCase						      =   True
         _lowerCAmelCase						      =   model_class(_lowerCAmelCase		)
         model.to(_lowerCAmelCase		)
         model.eval()
         with torch.no_grad():
             _lowerCAmelCase						      =   model(**self._prepare_for_class(_lowerCAmelCase      ,  _lowerCAmelCase		)		)
         _lowerCAmelCase						      =   outputs.attentions
         _lowerCAmelCase						      =   len(self.model_tester.depths		)
         self.assertEqual(len(_lowerCAmelCase		)      ,  _lowerCAmelCase		)

         # check that output_attentions also work using config
         del inputs_dict["output_attentions"]
         _lowerCAmelCase						      =   True
         _lowerCAmelCase						      =   config.window_size**2
         _lowerCAmelCase						      =   model_class(_lowerCAmelCase		)
         model.to(_lowerCAmelCase		)
         model.eval()
         with torch.no_grad():
             _lowerCAmelCase						      =   model(**self._prepare_for_class(_lowerCAmelCase      ,  _lowerCAmelCase		)		)
         _lowerCAmelCase						      =   outputs.attentions
         self.assertEqual(len(_lowerCAmelCase		)      ,  _lowerCAmelCase		)

         self.assertListEqual(
             list(attentions[0].shape[-3:]		)      ,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,  )
         _lowerCAmelCase						      =   len(_lowerCAmelCase		)

         # Check attention is always last and order is fine
         _lowerCAmelCase						      =   True
         _lowerCAmelCase						      =   True
         _lowerCAmelCase						      =   model_class(_lowerCAmelCase		)
         model.to(_lowerCAmelCase		)
         model.eval()
         with torch.no_grad():
             _lowerCAmelCase						      =   model(**self._prepare_for_class(_lowerCAmelCase      ,  _lowerCAmelCase		)		)

         if hasattr(self.model_tester      ,  "num_hidden_states_types"		):
             _lowerCAmelCase						      =   self.model_tester.num_hidden_states_types
         else:
             # also another +1 for reshaped_hidden_states
             _lowerCAmelCase						      =   2
         self.assertEqual(out_len + added_hidden_states      ,  len(_lowerCAmelCase		)		)

         _lowerCAmelCase						      =   outputs.attentions

         self.assertEqual(len(_lowerCAmelCase		)      ,  _lowerCAmelCase		)

         self.assertListEqual(
             list(self_attentions[0].shape[-3:]		)      ,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,  )




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				List[Any]:
     _lowerCAmelCase						      =   model_class(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()

     with torch.no_grad():
         _lowerCAmelCase						      =   model(**self._prepare_for_class(_lowerCAmelCase      ,  _lowerCAmelCase		)		)

     _lowerCAmelCase						      =   outputs.hidden_states

     _lowerCAmelCase						      =   getattr(
         self.model_tester      ,  "expected_num_hidden_layers"      ,  len(self.model_tester.depths		) + 1		)
     self.assertEqual(len(_lowerCAmelCase		)      ,  _lowerCAmelCase		)

     # Swinv2 has a different seq_length
     _lowerCAmelCase						      =   (
         config.patch_size
         if isinstance(config.patch_size      ,  collections.abc.Iterable		)
         else (config.patch_size, config.patch_size)
     )

     _lowerCAmelCase						      =   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

     self.assertListEqual(
         list(hidden_states[0].shape[-2:]		)      ,  [num_patches, self.model_tester.embed_dim]      ,  )

     _lowerCAmelCase						      =   outputs.reshaped_hidden_states
     self.assertEqual(len(_lowerCAmelCase		)      ,  _lowerCAmelCase		)

     _lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase						      =   reshaped_hidden_states[0].shape
     _lowerCAmelCase						      =   (
         reshaped_hidden_states[0].view(_lowerCAmelCase      ,  _lowerCAmelCase      ,  height * width		).permute(0      ,  2      ,  1		)
     )
     self.assertListEqual(
         list(reshaped_hidden_states.shape[-2:]		)      ,  [num_patches, self.model_tester.embed_dim]      ,  )




 def 				_snake_case (						self		)     ->				int:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs_for_common()

     _lowerCAmelCase						      =   (
         self.model_tester.image_size
         if isinstance(self.model_tester.image_size      ,  collections.abc.Iterable		)
         else (self.model_tester.image_size, self.model_tester.image_size)
     )

     for model_class in self.all_model_classes:
         _lowerCAmelCase						      =   True
         self.check_hidden_states_output(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         _lowerCAmelCase						      =   True

         self.check_hidden_states_output(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				str:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs_for_common()
     _lowerCAmelCase						      =   3

     _lowerCAmelCase						      =   (
         self.model_tester.image_size
         if isinstance(self.model_tester.image_size      ,  collections.abc.Iterable		)
         else (self.model_tester.image_size, self.model_tester.image_size)
     )
     _lowerCAmelCase						      =   (
         config.patch_size
         if isinstance(config.patch_size      ,  collections.abc.Iterable		)
         else (config.patch_size, config.patch_size)
     )

     _lowerCAmelCase						      =   image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
     _lowerCAmelCase						      =   image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

     for model_class in self.all_model_classes:
         _lowerCAmelCase						      =   True
         self.check_hidden_states_output(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  (padded_height, padded_width)		)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         _lowerCAmelCase						      =   True
         self.check_hidden_states_output(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  (padded_height, padded_width)		)




 def 				_snake_case (						self		)     ->				Tuple:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase		)




 @slow
 def 				_snake_case (						self		)     ->				Optional[int]:
     for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase						      =   SwinvaModel.from_pretrained(_lowerCAmelCase		)
         self.assertIsNotNone(_lowerCAmelCase		)





 def 				_snake_case (						self		)     ->				List[str]:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs_for_common()

     _lowerCAmelCase						      =   _config_zero_init(_lowerCAmelCase		)
     for model_class in self.all_model_classes:
         _lowerCAmelCase						      =   model_class(config=_lowerCAmelCase		)
         for name, param in model.named_parameters():
             if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
                 self.assertIn(
                     ((param.data.mean() * 1E9).round() / 1E9).item()      ,  [0.0, 1.0]      ,  msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''      ,  )







@require_vision
@require_torch
class  lowerCAmelCase_		(		unittest.TestCase		):




 @cached_property
 def 				_snake_case (						self		)     ->				Optional[Any]:
     return (
         AutoImageProcessor.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256"		)
         if is_vision_available()
         else None
     )





 @slow
 def 				_snake_case (						self		)     ->				str:
     _lowerCAmelCase						      =   SwinvaForImageClassification.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256"		).to(
         _lowerCAmelCase		)
     _lowerCAmelCase						      =   self.default_image_processor

     _lowerCAmelCase						      =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
     _lowerCAmelCase						      =   image_processor(images=_lowerCAmelCase      ,  return_tensors="pt"		).to(_lowerCAmelCase		)

     # forward pass
     with torch.no_grad():
         _lowerCAmelCase						      =   model(**_lowerCAmelCase		)

     # verify the logits
     _lowerCAmelCase						      =   torch.Size((1, 1000)		)
     self.assertEqual(outputs.logits.shape      ,  _lowerCAmelCase		)
     _lowerCAmelCase						      =   torch.tensor([-0.3947, -0.4306, 0.0026]		).to(_lowerCAmelCase		)
     self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,  _lowerCAmelCase      ,  atol=1E-4		)		)



from typing import Any




class   _UpperCamelCase :


			"""simple docstring"""






			def __init__(  self ,  lowerCAmelCase__		)	->   int:

							'''simple docstring'''


							__lowercase					     =				data
							__lowercase					     =				None





			def __repr__(  self		)	->   str:

							'''simple docstring'''


							return F"Node({self.data})"




class   _UpperCamelCase :


			"""simple docstring"""






			def __init__(  self		)	->   Optional[Any]:

							'''simple docstring'''


							__lowercase					     =				None





			def __iter__(  self		)	->   Any:

							'''simple docstring'''


							__lowercase					     =				self.head
							while node:
											yield node.data
											__lowercase					     =				node.next





			def __len__(  self		)	->   int:

							'''simple docstring'''


							return sum(1 for _ in self		)





			def __repr__(  self		)	->   str:

							'''simple docstring'''


							return "->".join([str(lowerCAmelCase__		) for item in self]		)





			def __getitem__(  self ,  lowerCAmelCase__		)	->   Any:

							'''simple docstring'''


							if not 0 <= index < len(self		):
											raise ValueError('''list index out of range.'''		)
							for i, node in enumerate(self		):
											if i == index:
															return node
							return None





			def __setitem__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   None:

							'''simple docstring'''


							if not 0 <= index < len(self		):
											raise ValueError('''list index out of range.'''		)
							__lowercase					     =				self.head
							for _ in range(lowerCAmelCase__		):
											__lowercase					     =				current.next
							__lowercase					     =				data





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   None:

							'''simple docstring'''


							self.insert_nth(len(self		) ,  lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   None:

							'''simple docstring'''


							self.insert_nth(0 ,  lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   None:

							'''simple docstring'''


							if not 0 <= index <= len(self		):
											raise IndexError('''list index out of range'''		)
							__lowercase					     =				Node(lowerCAmelCase__		)
							if self.head is None:
											__lowercase					     =				new_node
							elif index == 0:
											__lowercase					     =				self.head  # link new_node to head
											__lowercase					     =				new_node
							else:
											__lowercase					     =				self.head
											for _ in range(index - 1		):
															__lowercase					     =				temp.next
											__lowercase					     =				temp.next
											__lowercase					     =				new_node





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   None:  # print every node data

							'''simple docstring'''


							print(self		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Any:

							'''simple docstring'''


							return self.delete_nth(0		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Any:  # delete from tail

							'''simple docstring'''


							return self.delete_nth(len(self		) - 1		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ = 0		)	->   Any:

							'''simple docstring'''


							if not 0 <= index <= len(self		) - 1:  # test if index is valid
											raise IndexError('''List index out of range.'''		)
							__lowercase					     =				self.head  # default first node
							if index == 0:
											__lowercase					     =				self.head.next
							else:
											__lowercase					     =				self.head
											for _ in range(index - 1		):
															__lowercase					     =				temp.next
											__lowercase					     =				temp.next
											__lowercase					     =				temp.next.next
							return delete_node.data





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   bool:

							'''simple docstring'''


							return self.head is None





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   None:

							'''simple docstring'''


							__lowercase					     =				None
							__lowercase					     =				self.head

							while current:
											# Store the current node's next node.
											__lowercase					     =				current.next
											# Make the current node's next point backwards
											__lowercase					     =				prev
											# Make the previous node be the current node
											__lowercase					     =				current
											# Make the current node the next node (to progress iteration)
											__lowercase					     =				next_node
							# Return prev in order to put the head at the end
							__lowercase					     =				prev





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				LinkedList()
				assert linked_list.is_empty() is True
				assert str(lowercase				) == ""

				try:
								linked_list.delete_head()
								raise AssertionError  # This should not happen.
				except IndexError:
								assert True  # This should happen.

				try:
								linked_list.delete_tail()
								raise AssertionError  # This should not happen.
				except IndexError:
								assert True  # This should happen.

				for i in range(10				):
								assert len(lowercase				) == i
								linked_list.insert_nth(lowercase				,     i + 1				)
				assert str(lowercase				) == "->".join(str(lowercase				) for i in range(1				,     11				)				)

				linked_list.insert_head(0				)
				linked_list.insert_tail(11				)
				assert str(lowercase				) == "->".join(str(lowercase				) for i in range(0				,     12				)				)

				assert linked_list.delete_head() == 0
				assert linked_list.delete_nth(9				) == 10
				assert linked_list.delete_tail() == 11
				assert len(lowercase				) == 9
				assert str(lowercase				) == "->".join(str(lowercase				) for i in range(1				,     10				)				)

				assert all(linked_list[i] == i + 1 for i in range(0				,     9				)				) is True

				for i in range(0				,     9				):
								__lowercase					     =				-i
				assert all(linked_list[i] == -i for i in range(0				,     9				)				) is True

				linked_list.reverse()
				assert str(lowercase				) == "->".join(str(lowercase				) for i in range(-8				,     1				)				)





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				[
				    -9,
				    100,
				    Node(77345112				),
				    '''dlrow olleH''',
				    7,
				    5555,
				    0,
				    -192.55555,
				    '''Hello, world!''',
				    77.9,
				    Node(10				),
				    None,
				    None,
				    12.20,
				]
				__lowercase					     =				LinkedList()

				for i in test_input:
								linked_list.insert_tail(lowercase				)

				# Check if it's empty or not
				assert linked_list.is_empty() is False
				assert (
				    str(lowercase				) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
				    "-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
				)

				# Delete the head
				__lowercase					     =				linked_list.delete_head()
				assert result == -9
				assert (
				    str(lowercase				) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
				    "Hello, world!->77.9->Node(10)->None->None->12.2"
				)

				# Delete the tail
				__lowercase					     =				linked_list.delete_tail()
				assert result == 12.2
				assert (
				    str(lowercase				) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
				    "Hello, world!->77.9->Node(10)->None->None"
				)

				# Delete a node in specific location in linked list
				__lowercase					     =				linked_list.delete_nth(10				)
				assert result is None
				assert (
				    str(lowercase				) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
				    "Hello, world!->77.9->Node(10)->None"
				)

				# Add a Node instance to its head
				linked_list.insert_head(Node('''Hello again, world!'''				)				)
				assert (
				    str(lowercase				)
				    == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
				    "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
				)

				# Add None to its tail
				linked_list.insert_tail(lowercase				)
				assert (
				    str(lowercase				)
				    == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
				    "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
				)

				# Reverse the linked list
				linked_list.reverse()
				assert (
				    str(lowercase				)
				    == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
				    "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
				)





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				from doctest import testmod

				testmod()

				__lowercase					     =				LinkedList()
				linked_list.insert_head(input('''Inserting 1st at head '''				).strip()				)
				linked_list.insert_head(input('''Inserting 2nd at head '''				).strip()				)
				print('''\nPrint list:'''				)
				linked_list.print_list()
				linked_list.insert_tail(input('''\nInserting 1st at tail '''				).strip()				)
				linked_list.insert_tail(input('''Inserting 2nd at tail '''				).strip()				)
				print('''\nPrint list:'''				)
				linked_list.print_list()
				print('''\nDelete head'''				)
				linked_list.delete_head()
				print('''Delete tail'''				)
				linked_list.delete_tail()
				print('''\nPrint list:'''				)
				linked_list.print_list()
				print('''\nReverse linked list'''				)
				linked_list.reverse()
				print('''\nPrint list:'''				)
				linked_list.print_list()
				print('''\nString representation of linked list:'''				)
				print(lowercase				)
				print('''\nReading/changing Node data using indexing:'''				)
				print(F"Element at Position 1: {linked_list[1]}"				)
				__lowercase					     =				input('''Enter New Value: '''				).strip()
				print('''New list:'''				)
				print(lowercase				)
				print(F"length of linked_list is : {len(lowercase				)}"				)


if __name__ == "__main__":
		main()